Unleashing Unlabeled Data: A Paradigm for Cross-View Geo-Localization

Understand

This paper investigates the effective utilization of unlabeled data for large-area cross-view geo-localization (CVGL), encompassing both unsupervised and semi-supervised settings.

• Common approaches to CVGL rely on ground-satellite image pairs and employ label-driven supervised training.
• However, the cost of collecting precise cross-view image pairs hinders the deployment of CVGL in real-life scenarios.
• Without the pairs, CVGL will be more challenging to handle the significant imaging and spatial gaps between ground and satellite images.